20260506 Rlc

Our paper Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL was accepted to RLC 2026. We introduce ERAHBO, a Bayesian optimization method that efficiently searches for reinforcement-learning hyperparameters with high average performance and low variability by modeling heteroscedastic outcomes and adaptively deciding when additional training runs are needed.